2017
DOI: 10.1080/03610918.2017.1371750
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Number of predictors and multicollinearity: What are their effects on error and bias in regression?

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Cited by 111 publications
(53 citation statements)
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References 13 publications
(24 reference statements)
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“…Regression coefficients are presented in Table 2. As indicated in Table 2, multicollinearity was not a concern, as tolerance values were 0.96 and higher while VIF < 1.35 (Lavery et al, 2019). The analysis revealed that higher OCB scores, as reported by the supervisors, predicted higher levels of WFF (β = 0.20, p < 0.05), thus supporting H1.…”
Section: Confirmatory Factor Analysissupporting
confidence: 56%
“…Regression coefficients are presented in Table 2. As indicated in Table 2, multicollinearity was not a concern, as tolerance values were 0.96 and higher while VIF < 1.35 (Lavery et al, 2019). The analysis revealed that higher OCB scores, as reported by the supervisors, predicted higher levels of WFF (β = 0.20, p < 0.05), thus supporting H1.…”
Section: Confirmatory Factor Analysissupporting
confidence: 56%
“…The degree of multicollinearity was assessed with the tolerance value and the variance inflation factor (VIF). When the tolerance value was >0.10 and the VIF was <10 for the predictor variables, there were no serious problems with multicollinearity ( López, 1998 ; Dormann et al, 2013 ; Lavery et al, 2017 ). A p -value of 0.005 represented significant differences.…”
Section: Methodsmentioning
confidence: 99%
“…Robust standard errors were used for the Poisson GEE given the clustering for the incidence of diarrhea, ARI and malnutrition within districts [ 62 , 63 ]. Multicollinearity was assessed using correlation coefficients and the Variance Inflation Factor (VIF) with cut-off values of ≥0.8 and ≥ 10, respectively [ 64 ].…”
Section: Methodsmentioning
confidence: 99%